Are you watching new customers sign up every week and still finding that your revenue is sitting flat? Do you feel that something is quietly going wrong but the dashboard does not show you what it is? Well then, churn rate analysis is exactly the exercise you need. Churn rate analysis means measuring the number of customers who stop paying you in a given period, and then studying the data to understand who exactly is leaving and why they are leaving.
The measuring part is simple, it will not take more than a few minutes and one small formula. The studying part takes more effort, however, it is the part that holds most of the value. Many companies calculate the churn rate every month and stop with that, which is honestly a missed opportunity, because the churn rate on its own only tells you that customers are leaving. It does not tell you which customers are leaving, at what point of their journey they are leaving, or what you could do about it.
This article describes in detail the churn formulas, a worked cohort analysis example, the industry benchmarks, and the support signals that may appear before a customer decides to leave. Go through it to have a better understanding of the process:
What Is Churn Rate?
Churn rate refers to the percentage of customers who stopped paying for your product within a given time window. The window can be a month, a quarter, or a year, you can choose it according to the way your customers buy. Subscription businesses usually track churn every month. Businesses that work with annual contracts track it yearly instead, an annual customer gets the chance to leave only once a year, so a monthly churn number will not offer them meaningful information.
You must know that there are two types of churn: customer churn and revenue churn. Customer churn counts the number of customers lost, whereas revenue churn counts the amount of recurring revenue lost. It is recommended that you track both, the two numbers tell very different stories. In case you lost 5% of your customers but only 1% of your revenue, then it means that the customers who left were small accounts, which is survivable. However, in case you lost 2% of your customers but a massive 9% of your revenue, then it means that a large account has left, and that situation requires a very different response, a personal phone call and not a spreadsheet.
How To Calculate Churn Rate
Calculating the churn rate is easy, and you can get it done in a few simple steps. To calculate the customer churn rate, you must divide the number of customers lost in the period by the number of customers you had at the start of the period, and then multiply the value by 100. Isn't that simple?
For example, say you started June with 400 customers, and 22 of them left before the end of the month. Divide 22 by 400, multiply the value by 100, and the monthly customer churn rate you get is 5.5%.
However, there are two rules that you must follow to keep this number honest. Firstly, the new customers who joined during June should not be included in the starting count, they were not present at the start of the period. Secondly, a customer who joined in June and also left in June is still a real loss, so you must count them. Many teams handle this by adding the mid-period signups to the starting count before dividing, and this adjustment is acceptable as long as you apply it the very same way every month.
The gross revenue churn is calculated in a similar way, divide the recurring revenue lost in the period by the recurring revenue you had at the start, and multiply by 100. There is also a net version that goes by the name of net revenue churn. In this version, the expansion revenue from existing customers, such as upgrades, added workspaces, and more, is subtracted from the revenue you lost. Strong companies achieve negative net revenue churn, which means the expansion revenue is greater than the revenue lost, and this is one of the first numbers that subscription investors get to ask about.
Moreover, in case your business is seasonal, compare each month against the same month of the previous year and not against the previous month. A tax software company will always see higher churn in May, that is a seasonal pattern and not a business problem, hence, your analysis must account for it.
Let's quickly go through the details once more:
- Count the number of customers you lost in the period.
- Divide the value by the number of customers you had at the start of the period.
- Multiply the value by 100 to get the customer churn rate percentage.
- Calculate the revenue churn the same way, using the recurring revenue values instead.
- In case your business is seasonal, compare each month against the same month of the previous year.
- Make sure you apply the same counting rules every month, consistency matters more than the convention.
Churn Rate Analysis: A Worked Cohort Example
This is the part that most churn guides skip! A cohort analysis groups your customers based on the month they signed up, and then follows each group over time, increasing the chances of you finding the exact point where the churn is happening. Instead of one blended churn number, you get to see a table that shows you at which point of the customer journey the customers are leaving.
For example, say the January signups dropped to 71% by their second month, and 66% by the third. February behaved in a similar way, with a steep drop in the first month and a calmer slope after that. However, the March signups kept a whopping 83% of the customers through the very same period. Do you see the difference? The question changes from a vague one, which is why do customers churn, to a specific one, which is what changed in March, and somebody in your company can answer the second question in an afternoon. Perhaps new onboarding emails were introduced, or perhaps the support team started answering setup questions within a day instead of three days.
As mentioned earlier, the studying part holds most of the value, and this is the core method involved in it: you cut the single churn number into groups, look for the group that behaves differently, and then find out what was different about that group's experience. It is recommended that you start with cohorts by signup month, they are the easiest to build. Once done, you can cut the data by plan size, by acquisition channel, by country, and by support history. The method remains the same for every cut, so you can keep repeating it until you find the group that stands out.
The Customer Segments That Reveal The Most
After the signup cohorts, three segments tend to offer the most useful findings. Brief descriptions of these segments are given below:
The first segment is by plan. In case your cheapest plan churns at three times the rate of the other plans, it can mean two separate things. It can mean that small customers are less stable, which is normal. Or it can mean that the plan is attracting customers the product was not built for, and your marketing team must get to know about it.
The second segment is by onboarding completion. Split your customers based on whether they finished the setup steps, such as connecting their email, inviting a teammate, and more. The retention gap between the customers who completed the onboarding and the customers who did not is usually the widest gap you will find in the entire dataset. It also hands you a clear action item, which is to get more customers through the onboarding, increasing the chances of the new customers staying with you for longer.
The third segment is by support history, and this is the segment we recommend the most. Match your churned customer list against your helpdesk records, and check two things: how many of the churned customers contacted support in their last sixty days, and how long they waited for a first reply. Most teams find that the churned customers fall into two groups. The first group went quiet, no tickets, no logins, they drifted away silently. The second group is different, they wrote to support, waited too long for an answer, wrote again, and then left. You must note that the second group was reachable the entire time! They told you they were struggling, in writing, with a date on it. In case your support tool cannot show you the reply times and ticket history of each customer, then this analysis is not possible at all. Our guide to help desk software describes in detail what to look for in such a tool, go through it to have a better understanding.
What Is A Good Churn Rate? Industry Benchmarks
Every company asks this question, so here are the commonly cited reference points. According to Recurly's benchmark research, the average monthly churn for subscription businesses is around 5% to 6%. The monthly churn for B2B products is roughly 4% to 5%, and for B2C products, it is closer to 7%. KeyBanc's annual SaaS survey reports that the median annual gross revenue churn is in the low teens. Moreover, the working target you will hear from practitioners for a small B2B product is 3% monthly or below.
It is essential to note that these are blended values obtained from companies of every size, industry, and business model, hence, they are useful for direction only. The most reliable comparison is your own churn rate against your own churn rate from the previous quarter. In case you run a B2B product and your monthly churn is 9%, you do not need more benchmarks, you need the cohort analysis described above.
It is also worth understanding why churn deserves so much attention. Research by Bain & Company found that a 5% improvement in customer retention could increase profits by 25% to 95%. Additionally, the widely quoted figure states that acquiring a new customer costs five to twenty five times more than retaining an existing one. The exact numbers vary based on the industry, however, the direction has remained the same for decades. Keeping a customer is much cheaper than replacing one.
How To Reduce Customer Churn
The right fix depends on what your analysis finds. However, the findings tend to fall into three common patterns, and each pattern comes with a known response.
In case the cohort table shows a steep drop in the first month, it means that the product is hard to get started with. In this case, it is recommended that you rebuild your onboarding around the first key action that your retained customers took early. Measure activation instead of signups, and treat it as the main onboarding metric, increasing the chances of the new customers reaching the moment where the product clicks for them.
In case the churned customers cluster on one plan or one acquisition channel, the problem is upstream, in the type of customers you are attracting. The cheapest fix here is often changing the promise on your landing page rather than changing the product.
The third pattern is the support one. In case the support history shows that the churned customers waited too long for replies, you must set a first reply time target for every conversation, and staff your support team to meet the target. This fix pays off twice! The struggling customers get help while they are still your customers, and your support queue becomes an early warning system where every at-risk account raises its hand in writing before leaving.
Whatever the finding is, close the loop by talking to the customers themselves. A short and honest email to the churned customers, asking what happened, may get a reply from one in ten, and the replies you get will name the problems that your dashboards cannot see. One honest reply could be worth a month of chart analysis.
How Often Should You Run Churn Rate Analysis?
The basic churn calculation should be done every month, it is one formula applied to your billing data, and the trend line is only useful if you keep adding data points to it. As discussed before, the deep analysis, which includes the cohort tables and the segment cuts, needs enough churned customers to show trustworthy patterns, so it should be done every quarter. Also, the fixes you make after each analysis need a full quarter to show up in the retention numbers. In case you run the deep analysis every month, you will mostly get the same table with minor variations, and your team will end up explaining random noise in the monthly meetings.
A brief overview of the recommended schedule:
- Calculate the customer churn and revenue churn every month.
- Run the full cohort and segment analysis every quarter.
- Compare the year's cohorts side by side once a year, and use the comparison to plan the next year's retention work.
- Review the support history of the churned customers as part of every quarterly analysis.
- Email a sample of churned customers each quarter and read every reply.
This schedule keeps the analysis useful, and at the same time, it keeps the workload reasonable for your team.
When You Should Not Run The Full Analysis
Are you running a product with fewer than a hundred customers? Well then, the churn percentages are mostly statistical noise for you. Three customers leaving out of sixty is a 5% churn rate, however, it is really just three individual stories. At that size, skip the spreadsheets and simply talk to the three customers who left, you will learn more in an hour of conversation than a cohort table could tell you in a quarter. Percentages become useful at the scale where you can no longer know every account personally.
Track Your Churn Signals With Maxdesk
Are you tired of guessing why your customers leave? Don't worry, you do not need a dedicated analytics product to start churn rate analysis. Your billing export gives you the churn dates, and your helpdesk gives you the customer behaviour before each date. At Maxdesk, we keep that second half ready for you. The free plan offers every customer's full conversation history, first reply times, and SLA records, and there is no per-agent fee on any plan, your whole team can work together without the bill moving an inch.
You must note that the free plan is supported by ads and keeps a rolling 3-month history window. We prefer to state that plainly here rather than have you discover it later. In case a longer reporting history matters to you, you can go for the paid plans according to your preferences.
Getting started is simple and will not take more than a few minutes. All you need to do is create a workspace, connect your support address, and invite your team. That's it! Are you ready to try then?